It is widely known that when there are negative moving average errors, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and BIC tend to select a truncation lag that is very small. Furthermore, size distortions...

This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of non-stationarity in the data. We refer to it as PANIC‹ a 'Panel Analysis of Non-stationarity in Idiosyncratic and Common components'. PANIC consists of...

This paper studies the error in forecasting a dynamic time series with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model which detrends the data before estimating the dynamic parameters are much less precise than those based on an...

This paper uses a decomposition of the data into common and idiosyncratic components to develop procedures that test if these components satisfy the null hypothesis of stationarity. The decomposition also allows us to construct pooled tests that satisfy the cross-section independence assumption....

In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose some panel C(p) criteria and show that the number of...